The present disclosure relates generally to the field of natural language processing, and, more particularly, to using natural language processing in making recommendations to individuals and users.
As a field of computer science, natural language processing tends to focus on the relationships between computer systems and human languages. Many modern natural language processing algorithms are derived based on machine learning and rely greatly on statistical inferences. By analyzing large sets of real-world examples of natural language usages, a computer system may be able to glean sets of rules that guide the machine through a future analysis of natural language passages.
Recommendations and recommender systems make recommendations to users based on a comparison of the user's profile with profiles of other users who make use of a marketplace. However, these recommendations have historically been based off of the relationships between items. Typically, this has been in the form of “people who have bought this have also bought these items”. More advanced systems of recommendations look at the items themselves to determine if the items are related, and the user may be interested in the items based on a similarity between the item being looked at and these items. However, these recommendation systems are limited in that they are based on the needs of the merchant offering the recommendations and not based on other needs, wants or desires of the consumer. The goal of the recommendation systems is to have the user buy a particular item from the merchant.